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Article Dans Une Revue Journal of Micropalaeontology Année : 2020

Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

Résumé

Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convo-lutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the northeastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.
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hal-02997928 , version 1 (10-11-2020)

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Ross Marchant, Martin Tetard, Adnya Pratiwi, Michael Adebayo, Thibault de Garidel-Thoron. Automated analysis of foraminifera fossil records by image classification using a convolutional neural network. Journal of Micropalaeontology, 2020, 39 (2), pp.183-202. ⟨10.5194/jm-39-183-2020⟩. ⟨hal-02997928⟩
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